Generative Adversarial Networks (GANs) are proving to be a powerful indirect
genotype-to-phenotype mapping for evolutionary search, but they have
limitations. In particular, GAN output does not scale to arbitrary dimensions,
and there is no obvious way of combining multiple GAN outputs into a cohesive
whole, which would be useful in many areas, such as the generation of video
game levels. Game levels often consist of several segments, sometimes repeated
directly or with variation, organized into an engaging pattern. Such patterns
can be produced with Compositional Pattern Producing Networks (CPPNs).
Specifically, a CPPN can define latent vector GAN inputs as a function of
geometry, which provides a way to organize level segments output by a GAN into
a complete level. This new CPPN2GAN approach is validated in both Super Mario
Bros. and The Legend of Zelda. Specifically, divergent search via MAP-Elites
demonstrates that CPPN2GAN can better cover the space of possible levels. The
layouts of the resulting levels are also more cohesive and aesthetically
consistent.

通过将 Compositional Pattern Producing Networks 与 GAN 结合，定义 latent vectors 为一种以组织 level segment 的方式生成完整游戏级别的方法，并通过 MAP-Elites 进行验证。

将复合式模式生成网络和 GAN 结合，用于大规模图案生成的 CPPN2GAN

CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs  for Large-scale Pattern Generation

Compositional Pattern Producing Networks (CPPNs) are differentiable networks
that independently map (x, y) pixel coordinates to (r, g, b) colour values.
Recently, CPPNs have been used for creating interesting imagery for creative
purposes, e.g., neural art. However their architecture biases generated images
to be overly smooth, lacking high-frequency detail. In this work, we extend
CPPNs to explicitly model the frequency information for each pixel output,
capturing frequencies beyond the DC component. We show that our Fourier-CPPNs
(F-CPPNs) provide improved visual detail for image synthesis.

本论文提出了 Fourier-CPPNs（F-CPPNs），并将其用于创造性的目的，例如神经美术。F-CPPNs 以像素输出的频率信息为特征，明确地模型化频率信息，捕获 DC 分量以外的频率，为图像合成提供了改进的视觉细节。